OpenRaiser/Intern-Atlas huggingface dataset:
An Event-based Compositional Reasoning Approach for Concurrent Reactive Systems
Venue: arXiv cs.SE 2018
Year: 2018

Reactive systems are composed of a well defined set of input events that the system reacts with by executing an associated handler to each event. In concurrent environments, event handlers can interact with the execution of other programs such as hardware interruptions in preemptive systems, or other instances of the reactive system in multicore architectures. State of the art rely-guarantee based verification frameworks only focus on imperative programs, being difficult to capture in the rely and guarantee relations interactions with possible infinite sequences of event handlers, and the input arguments to event handlers. In this paper, we propose the formalisation in Isabelle/HOL of an event-based rely-guarantee approach for concurrent reactive systems. We develop a Pi-Core language which incorporates a concurrent imperative and system specification language by `events', and we build a rely-guarantee proof system for Pi-Core and prove its soundness. Our approach can deal with multicore and interruptible concurrency. We use two case studies to show this: an interruptible controller for stepper motors and an ARINC 653 multicore kernel, and prove the functional correctness and preservation of invariants of them in Isabelle/HOL.
A Temporally Sensitive Submodularity Framework for Timeline Summarization
Venue: arXiv cs.CL 2018
Year: 2018

Timeline summarization (TLS) creates an overview of long-running events via dated daily summaries for the most important dates. TLS differs from standard multi-document summarization (MDS) in the importance of date selection, interdependencies between summaries of different dates and by having very short summaries compared to the number of corpus documents. However, we show that MDS optimization models using submodular functions can be adapted to yield well-performing TLS models by designing objective functions and constraints that model the temporal dimension inherent in TLS. Importantly, these adaptations retain the elegance and advantages of the original MDS models (clear separation of features and inference, performance guarantees and scalability, little need for supervision) that current TLS-specific models lack. An open-source implementation of the framework and all models described in this paper is available online.
{É}tude pour l'analyse et l'optimisation du transport des personnes en situation de handicap
Venue: arXiv cs.CY 2018
Year: 2018

From 2010, the medical transport has become one of the top ten priorities of the risk management plan in France because of the increase in the cost. For social and medico-social institutions (MSI), this cost represents the second after that of the wages. In this context, the project NOMAd aims an overall improvement of the daily transport of people between their home and their (MSI). To this end, we propose the sharing of transport between several ESMS. This mutualization of transport makes possible to gather and optimize routes in a certain geographical area. The challenge is to improve economic performance while maintaining economic, social and environmental goals. From a scientific point of view, the studied problem is called the Time-Consistent-Dial-a-Ride Problem and aims to find a compromise between the objectives of the cost of transport and the consistency of the service. Given the complexity of the problem, we seek, first of all, to solve the problem for half a day. Then we consider the whole week. To solve these problems, we use the Large Neighborhood Search meta-heuristic and a master problem based on the Set Covering Problem.
An algebraic theory for data linkage
Venue: arXiv cs.LO 2018
Year: 2018

There are countless sources of data available to governments, companies, and citizens, which can be combined for good or evil. We analyse the concepts of combining data from common sources and linking data from different sources. We model the data and its information content to be found in a single source by a partial ordered monoid, and the transfer of information between sources by different types of morphisms. To capture the linkage between a family of sources, we use a form of Grothendieck construction to create a partial ordered monoid that brings together the global data of the family in a single structure. We apply our approach to database theory and axiomatic structures in approximate reasoning. Thus, partial ordered monoids provide a foundation for the algebraic study for information gathering in its most primitive form.
KTAN: Knowledge Transfer Adversarial Network
Venue: arXiv cs.LG 2018
Year: 2018

To reduce the large computation and storage cost of a deep convolutional neural network, the knowledge distillation based methods have pioneered to transfer the generalization ability of a large (teacher) deep network to a light-weight (student) network. However, these methods mostly focus on transferring the probability distribution of the softmax layer in a teacher network and thus neglect the intermediate representations. In this paper, we propose a knowledge transfer adversarial network to better train a student network. Our technique holistically considers both intermediate representations and probability distributions of a teacher network. To transfer the knowledge of intermediate representations, we set high-level teacher feature maps as a target, toward which the student feature maps are trained. Specifically, we arrange a Teacher-to-Student layer for enabling our framework suitable for various student structures. The intermediate representation helps the student network better understand the transferred generalization as compared to the probability distribution only. Furthermore, we infuse an adversarial learning process by employing a discriminator network, which can fully exploit the spatial correlation of feature maps in training a student network. The experimental results demonstrate that the proposed method can significantly improve the performance of a student network on both image classification and object detection tasks.
Testing Matrix Rank, Optimally
Venue: arXiv cs.DS 2018
Year: 2018

We show that for the problem of testing if a matrix $A \in F^{n \times n}$ has rank at most $d$, or requires changing an $ε$-fraction of entries to have rank at most $d$, there is a non-adaptive query algorithm making $\widetilde{O}(d^2/ε)$ queries. Our algorithm works for any field $F$. This improves upon the previous $O(d^2/ε^2)$ bound (SODA'03), and bypasses an $Ω(d^2/ε^2)$ lower bound of (KDD'14) which holds if the algorithm is required to read a submatrix. Our algorithm is the first such algorithm which does not read a submatrix, and instead reads a carefully selected non-adaptive pattern of entries in rows and columns of $A$. We complement our algorithm with a matching query complexity lower bound for non-adaptive testers over any field. We also give tight bounds of $\widetildeΘ(d^2)$ queries in the sensing model for which query access comes in the form of $\langle X_i, A\rangle:=tr(X_i^\top A)$; perhaps surprisingly these bounds do not depend on $ε$. We next develop a novel property testing framework for testing numerical properties of a real-valued matrix $A$ more generally, which includes the stable rank, Schatten-$p$ norms, and SVD entropy. Specifically, we propose a bounded entry model, where $A$ is required to have entries bounded by $1$ in absolute value. We give upper and lower bounds for a wide range of problems in this model, and discuss connections to the sensing model above.
Web Application for Collaborative Semantic Web Information Architecture
Venue: arXiv cs.CY 2018
Year: 2018

In this paper is analyzed the prototyping of the information visualization on a Web Application for community purposes in a collaborative environment representing an evolution of the actual social networks like Facebook, Instagram, Twitter, Linkedin, VirgilioPeople,... The intent of this work is to identify the most common features of Web App for the information visualization based on the Semantic Web and discuss how they support the user's requirements in a "collaborative" environment. A solution for the context-aware development of UI is based on "joint meaning" understood as a joint construal of the creator of the community contents and the user of the community contents thanks to the context and interface adaptation using the faced taxonomy with the Semantic Web. A proof-of concept prototype allows showing that the proposed methodological approach can also easily be applied to existing presentation components, built with different languages and/or component technologies.
Semantic Integration in the Information Flow Framework
Venue: arXiv cs.LO 2018
Year: 2018

The Information Flow Framework (IFF) is a descriptive category metatheory currently under development, which is being offered as the structural aspect of the Standard Upper Ontology (SUO). The architecture of the IFF is composed of metalevels, namespaces and meta-ontologies. The main application of the IFF is institutional: the notion of institutions and their morphisms are being axiomatized in the upper metalevels of the IFF, and the lower metalevel of the IFF has axiomatized various institutions in which semantic integration has a natural expression as the colimit of theories.
Dynamic Automatic Differentiation of GPU Broadcast Kernels
Venue: arXiv cs.MS 2018
Year: 2018

We show how forward-mode automatic differentiation (AD) can be employed within larger reverse-mode computations to dynamically differentiate broadcast operations in a GPU-friendly manner. Our technique fully exploits the broadcast Jacobian's inherent sparsity structure, and unlike a pure reverse-mode approach, this "mixed-mode" approach does not require a backwards pass over the broadcasted operation's subgraph, obviating the need for several reverse-mode-specific programmability restrictions on user-authored broadcast operations. Most notably, this approach allows broadcast fusion in primal code despite the presence of data-dependent control flow. We discuss an experiment in which a Julia implementation of our technique outperformed pure reverse-mode TensorFlow and Julia implementations for differentiating through broadcast operations within an HM-LSTM cell update calculation.
Quality Assessment for Tone-Mapped HDR Images Using Multi-Scale and Multi-Layer Information
Venue: arXiv cs.MM 2018
Year: 2018

Tone mapping operators and multi-exposure fusion methods allow us to enjoy the informative contents of high dynamic range (HDR) images with standard dynamic range devices, but also introduce distortions into HDR contents. Therefore methods are needed to evaluate tone-mapped image quality. Due to the complexity of possible distortions in a tone-mapped image, information from different scales and different levels should be considered when predicting tone-mapped image quality. So we propose a new no-reference method of tone-mapped image quality assessment based on multi-scale and multi-layer features that are extracted from a pre-trained deep convolutional neural network model. After being aggregated, the extracted features are mapped to quality predictions by regression. The proposed method is tested on the largest public database for TMIQA and compared to existing no-reference methods. The experimental results show that the proposed method achieves better performance.
Exchangeability and Kernel Invariance in Trained MLPs
Venue: arXiv cs.LG 2018
Year: 2018

In the analysis of machine learning models, it is often convenient to assume that the parameters are IID. This assumption is not satisfied when the parameters are updated through training processes such as SGD. A relaxation of the IID condition is a probabilistic symmetry known as exchangeability. We show the sense in which the weights in MLPs are exchangeable. This yields the result that in certain instances, the layer-wise kernel of fully-connected layers remains approximately constant during training. We identify a sharp change in the macroscopic behavior of networks as the covariance between weights changes from zero.
Malicious Web Domain Identification using Online Credibility and Performance Data by Considering the Class Imbalance Issue
Venue: arXiv cs.LG 2018
Year: 2018

Purpose: Malicious web domain identification is of significant importance to the security protection of Internet users. With online credibility and performance data, this paper aims to investigate the use of machine learning tech-niques for malicious web domain identification by considering the class imbalance issue (i.e., there are more benign web domains than malicious ones). Design/methodology/approach: We propose an integrated resampling approach to handle class imbalance by combining the Synthetic Minority Over-sampling TEchnique (SMOTE) and Particle Swarm Optimisation (PSO), a population-based meta-heuristic algorithm. We use the SMOTE for over-sampling and PSO for under-sampling. Findings: By applying eight well-known machine learning classifiers, the proposed integrated resampling approach is comprehensively examined using several imbalanced web domain datasets with different imbalance ratios. Com-pared to five other well-known resampling approaches, experimental results confirm that the proposed approach is highly effective. Practical implications: This study not only inspires the practical use of online credibility and performance data for identifying malicious web domains, but also provides an effective resampling approach for handling the class imbal-ance issue in the area of malicious web domain identification. Originality/value: Online credibility and performance data is applied to build malicious web domain identification models using machine learning techniques. An integrated resampling approach is proposed to address the class im-balance issue. The performance of the proposed approach is confirmed based on real-world datasets with different imbalance ratios.
QANet: Tensor Decomposition Approach for Query-based Anomaly Detection in Heterogeneous Information Networks
Venue: arXiv cs.SI 2018
Year: 2018

Complex networks have now become integral parts of modern information infrastructures. This paper proposes a user-centric method for detecting anomalies in heterogeneous information networks, in which nodes and/or edges might be from different types. In the proposed anomaly detection method, users interact directly with the system and anomalous entities can be detected through queries. Our approach is based on tensor decomposition and clustering methods. We also propose a network generation model to construct synthetic heterogeneous information network to test the performance of the proposed method. The proposed anomaly detection method is compared with state-of-the-art methods in both synthetic and real-world networks. Experimental results show that the proposed tensor-based method considerably outperforms the existing anomaly detection methods.
Flow Motifs in Interaction Networks
Venue: arXiv cs.SI 2018
Year: 2018

Many real-world phenomena are best represented as interaction networks with dynamic structures (e.g., transaction networks, social networks, traffic networks). Interaction networks capture flow of data which is transferred between their vertices along a timeline. Analyzing such networks is crucial toward comprehend- ing processes in them. A typical analysis task is the finding of motifs, which are small subgraph patterns that repeat themselves in the network. In this paper, we introduce network flow motifs, a novel type of motifs that model significant flow transfer among a set of vertices within a constrained time window. We design an algorithm for identifying flow motif instances in a large graph. Our algorithm can be easily adapted to find the top-k instances of maximal flow. In addition, we design a dynamic programming module that finds the instance with the maximum flow. We evaluate the performance of the algorithm on three real datasets and identify flow motifs which are significant for these graphs. Our results show that our algorithm is scalable and that the real networks indeed include interesting motifs, which appear much more frequently than in randomly generated networks having similar characteristics.
Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

The Copernicus Sentinel-2 program now provides multispectral images at a global scale with a high revisit rate. In this paper we explore the usage of convolutional neural networks for urban change detection using such multispectral images. We first present the new change detection dataset that was used for training the proposed networks, which will be openly available to serve as a benchmark. The Onera Satellite Change Detection (OSCD) dataset is composed of pairs of multispectral aerial images, and the changes were manually annotated at pixel level. We then propose two architectures to detect changes, Siamese and Early Fusion, and compare the impact of using different numbers of spectral channels as inputs. These architectures are trained from scratch using the provided dataset.
Probabilistic Matrix Factorization with Personalized Differential Privacy
Venue: arXiv cs.CR 2018
Year: 2018

Probabilistic matrix factorization (PMF) plays a crucial role in recommendation systems. It requires a large amount of user data (such as user shopping records and movie ratings) to predict personal preferences, and thereby provides users high-quality recommendation services, which expose the risk of leakage of user privacy. Differential privacy, as a provable privacy protection framework, has been applied widely to recommendation systems. It is common that different individuals have different levels of privacy requirements on items. However, traditional differential privacy can only provide a uniform level of privacy protection for all users. In this paper, we mainly propose a probabilistic matrix factorization recommendation scheme with personalized differential privacy (PDP-PMF). It aims to meet users' privacy requirements specified at the item-level instead of giving the same level of privacy guarantees for all. We then develop a modified sampling mechanism (with bounded differential privacy) for achieving PDP. We also perform a theoretical analysis of the PDP-PMF scheme and demonstrate the privacy of the PDP-PMF scheme. In addition, we implement the probabilistic matrix factorization schemes both with traditional and with personalized differential privacy (DP-PMF, PDP-PMF) and compare them through a series of experiments. The results show that the PDP-PMF scheme performs well on protecting the privacy of each user and its recommendation quality is much better than the DP-PMF scheme.
Deep Person Re-identification for Probabilistic Data Association in Multiple Pedestrian Tracking
Venue: arXiv cs.CV 2018
Year: 2018

We present a data association method for vision-based multiple pedestrian tracking, using deep convolutional features to distinguish between different people based on their appearances. These re-identification (re-ID) features are learned such that they are invariant to transformations such as rotation, translation, and changes in the background, allowing consistent identification of a pedestrian moving through a scene. We incorporate re-ID features into a general data association likelihood model for multiple person tracking, experimentally validate this model by using it to perform tracking in two evaluation video sequences, and examine the performance improvements gained as compared to several baseline approaches. Our results demonstrate that using deep person re-ID for data association greatly improves tracking robustness to challenges such as occlusions and path crossings.
Mainumby: un Ayudante para la Traducción Castellano-Guaraní
Venue: arXiv cs.CL 2018
Year: 2018

A wide range of applications play an important role in the daily work of the modern human translator. However, the computational tools designed to aid in the process of translation only benefit translation from or to a small minority of the 7,000 languages of the world, those that we may call "privileged languages". As for those translators who work with the remaining languages, the marginalized languages in the digital world, they cannot benefit from the tools that are speeding up the production of translation in the privileged languages. We may ask whether it is possible to bridge the gap between what is available for these languages and for the marginalized ones. This paper proposes a framework for computer-assisted translation into marginalized languages and its implementation in a web application for Spanish-Guarani translation. The proposed system is based on a new theory for phrase-level translation in contexts where adequate bilingual corpora are not available: Translation by Generalized Segments (referred to as Minimal Dependency Translation in previous work).
Fast and Robust Multiple ColorChecker Detection using Deep Convolutional Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

ColorCheckers are reference standards that professional photographers and filmmakers use to ensure predictable results under every lighting condition. The objective of this work is to propose a new fast and robust method for automatic ColorChecker detection. The process is divided into two steps: (1) ColorCheckers localization and (2) ColorChecker patches recognition. For the ColorChecker localization, we trained a detection convolutional neural network using synthetic images. The synthetic images are created with the 3D models of the ColorChecker and different background images. The output of the neural networks are the bounding box of each possible ColorChecker candidates in the input image. Each bounding box defines a cropped image which is evaluated by a recognition system, and each image is canonized with regards to color and dimensions. Subsequently, all possible color patches are extracted and grouped with respect to the center's distance. Each group is evaluated as a candidate for a ColorChecker part, and its position in the scene is estimated. Finally, a cost function is applied to evaluate the accuracy of the estimation. The method is tested using real and synthetic images. The proposed method is fast, robust to overlaps and invariant to affine projections. The algorithm also performs well in case of multiple ColorCheckers detection.
Optimized Extreme Learning Machine for Power System Transient Stability Prediction Using Synchrophasors
Venue: arXiv cs.NE 2018
Year: 2018

A new optimized extreme learning machine- (ELM-) based method for power system transient stability prediction (TSP) using synchrophasors is presented in this paper. First, the input features symbolizing the transient stability of power systems are extracted from synchronized measurements. Then, an ELM classifier is employed to build the TSP model. And finally, the optimal parameters of the model are optimized by using the improved particle swarm optimization (IPSO) algorithm. The novelty of the proposal is in the fact that it improves the prediction performance of the ELM-based TSP model by using IPSO to optimize the parameters of the model with synchrophasors. And finally, based on the test results on both IEEE 39-bus system and a large-scale real power system, the correctness and validity of the presented approach are verified.
Deep Learning with the Random Neural Network and its Applications
Venue: arXiv cs.NE 2018
Year: 2018

The random neural network (RNN) is a mathematical model for an "integrate and fire" spiking network that closely resembles the stochastic behaviour of neurons in mammalian brains. Since its proposal in 1989, there have been numerous investigations into the RNN's applications and learning algorithms. Deep learning (DL) has achieved great success in machine learning. Recently, the properties of the RNN for DL have been investigated, in order to combine their power. Recent results demonstrate that the gap between RNNs and DL can be bridged and the DL tools based on the RNN are faster and can potentially be used with less energy expenditure than existing methods.